{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "感觉书中的代码有问题，要不就是书里的文字表述有问题。  \n",
    "说是属性对绘图，但实际的代码却是样本对（行）之间的绘制。  \n",
    "所以后面会先按照书中的描述，对行（样本）进行绘制，然后再对列（属性）进行绘制  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 载入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签，字体名称为win中中文字体对应的英文名\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_url = (\"https://archive.ics.uci.edu/ml/machine-learning-\"\n",
    "\"databases/undocumented/connectionist-bench/sonar/sonar.all-data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    # 第一个参数比较灵活，可以是url,也可是文件路径，或者IO等。\n",
    "    df_sonar = pd.read_csv(\"../../data/sonar.csv\", header=0)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    df_sonar = pd.read_csv(target_url, header=None, prefix='V')\n",
    "    df_sonar.to_csv(\"../../data/sonar.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V0</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V51</th>\n",
       "      <th>V52</th>\n",
       "      <th>V53</th>\n",
       "      <th>V54</th>\n",
       "      <th>V55</th>\n",
       "      <th>V56</th>\n",
       "      <th>V57</th>\n",
       "      <th>V58</th>\n",
       "      <th>V59</th>\n",
       "      <th>V60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       V0      V1      V2      V3      V4      V5      V6      V7      V8  \\\n",
       "0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "\n",
       "       V9  ...     V51     V52     V53     V54     V55     V56     V57  \\\n",
       "0  0.2111  ...  0.0027  0.0065  0.0159  0.0072  0.0167  0.0180  0.0084   \n",
       "1  0.2872  ...  0.0084  0.0089  0.0048  0.0094  0.0191  0.0140  0.0049   \n",
       "2  0.6194  ...  0.0232  0.0166  0.0095  0.0180  0.0244  0.0316  0.0164   \n",
       "3  0.1264  ...  0.0121  0.0036  0.0150  0.0085  0.0073  0.0050  0.0044   \n",
       "4  0.4459  ...  0.0031  0.0054  0.0105  0.0110  0.0015  0.0072  0.0048   \n",
       "\n",
       "      V58     V59  V60  \n",
       "0  0.0090  0.0032    R  \n",
       "1  0.0052  0.0044    R  \n",
       "2  0.0095  0.0078    R  \n",
       "3  0.0040  0.0117    R  \n",
       "4  0.0107  0.0094    R  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sonar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行（样本对）之间的交会图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uLczl2tWLmZtppSSZm2nl2tWLmjABANyFrroHl1J+LMlskgeT/OUk/0OSP9fHugB67iA0DuIomqWFOSEVAKAHuj3y5mKt9Uop5Xqt9elSyn/X16oA+kSYBAAYLd0uD36+lPLDSe4vpfzFJM/1sSYAAABI0n1o/c4kv5fk15O8Psl39a0iAAAA2NfV8uBaayfJ3+xzLQAAAHCII28AAABorBPvtJZSfi1JvXM4Sa21vqNvVQEAAEBOCa211kcGVQgAAADcyfJgAAAAGqvbc1pTSllI8kCSz9Zat/pWEXCi9c12Vje2c3Onk9mZVpYX5507CgDA2OoqtJZS/laSr0rym0n+s1LKv6i1/jd9rQx4lfXNdlbWttLZ3UuStHc6WVm79RmS4AoAwDjq9k7rv19rffjgm1LKJ/pUD3CC1Y3t24H1QGd3L6sb20IrAABjqdvQ+rullPckeSbJW5P8v6WUf6/W+jv9Kw1GUz+X797c6ZxpHAAARl23ofXFJN+4/78k+TdJ3p/ku/tQE4ysfi/fnZ1ppX1EQJ2daZ37uQEAoIm67R78G/v/LXn5nFaBFe5w0vLdXlhenE9reurQWGt6KsuL8z15fgAAaJpu77S+J8mfT7J32kSYZP1evntwt1b3YAAAJkXXe1qT/MMkz2b/TmuSd/SrKBhVg1i+u7QwJ6QCADAxul0ePJ3kYq31HbXWR2qtAiscwfJdAADorW7vtH5Zkn9aSvndgwHBFV7N8l0AAOitrkJrrfUtpZQ/luRgjaPfwOEYlu8CAEDvdBVaSykfSfJAkjck+YPc2tP6cP/KAgAAgO73tH5lkncm+WySb0jyhb5VBAAAAPu6Da1/mOQ/TDKV5F1J7u9bRQAAALCv29D67Uk+k+SHkvzxJP9l3yoCAACAfaeG1lLKW2qtv5/k/07y9Un+ZZJ/2u/CAAAA4MTQWkr5qST/+f63P5pb+1ovJPmZPtcFAAAAp3YP/opa6yOllK9O8o4kb6q11lLK9QHUBgAAwIQ7LbS+UEr5b5N8a5K/muTeUsrV/pcFAAAAp4fWv5DkvUmu1Vp/af+O69ck+Y6+VwZAY61vtrO6sZ2bO53MzrSyvDifpYW5YZcFAIyhE0NrrfUPkvyPr/j+s0ke63dRADTX+mY7K2tb6ezuJUnaO52srG0lieAKAPTcaXdaYWS5EwT9sbqxfTuwHujs7mV1Y9u/MQCg54RWxpI7QdA/N3c6ZxoHADiPU89phVF00p0g4HxmZ1pnGgcAOA+hlbHkThD0z/LifFrTU4fGWtNTWV6cH1JFAMA4O3F5cCnl6497rNb68d6XA70xO9NK+4iA6k4QnN/BEnt7xgGAQThtT+sj+//9hiT/LsmNJG9O8rokb+9fWXA+y4vzh/a0Ju4EQS8tLcwJqQDAQJx25M1fSZJSyq/WWr/xYLyUcr3fhcF5uBMEAADjodvuwV8opfxgkt9M8jV9rAd6xp0gAAAYfd02YnpXki9O8u4kX7L/PQAAAPRVV3daa607ST7Y31IAAADgsK7utJZSfqXfhQAAAMCdul0evFVK+ZZun7SU8pFSyidLKY+fMu+NpZTNbp8XAACAydJtaP0PkvxvpZR/Ukr5tZO6B5dSriaZqrW+LclsKeWhE573rydxcCYAAABH6nZP6yOnz7rtSpKn9r++nuThJJ+5c1Ip5R1Jfj/Jc2d4bgAAACbIiXdaSyl/tJTyaCnlvlJKq5TyrlLKo6c8571J2vtfv5jkjUc87xcl+eEkj53y+u8rpdwopdx4/vnnT3lZAAAAxs2xobWU8keT/FqSb0zydJJfSHIxyTeXUv72Cc/5Ul5e8nvfMa/xWJIf3+9KfKxa65O11ku11ksXLlw4aSoAAABj6KQ7rQtJfqbWupzkLyfZrrX+cK31B5P8iRP+3DO5tSQ4Sd6U5HNHzPkzSb6/lPKxJG8upfzdsxYOAADA+DtpT+szSX6klPKrtdaPJ/l4kpRS3pvk357w59aTfKKUMpvknUneU0r5QK31difhWuvXH3xdSvlYrfV7z/F3AAAAYEwdG1prrS/sdwL+2jse+vIk337Cn3uxlHIlyaNJPlhrfS7Jp0+Yf+UM9QIAADBBTuwevB84n7tj7IOnPWmt9YW83EEYAAAA7kq357QCAADAwHV1TisAg7e+2c7qxnZu7nQyO9PK8uJ8lhbmhl0WAMBACa0ADbS+2c7K2lY6u3tJkvZOJytrW0kiuAIAE8XyYIAGWt3Yvh1YD3R297K6sT2kigAAhkNoBWigmzudM40DAIwroRWggWZnWmcaBwAYV0IrQAMtL86nNT11aKw1PZXlxfkhVXRrn+3lJ67nwceezuUnrmd9sz20WgCAyaERE0ADHTRbakr3YI2hAIBhEVoBGmppYa4xgfCkxlBNqREAGE+WBwNwKo2hAIBhEVoBOJXGUADAsAitAJyqiY2hAIDJYE8rAKdqWmMoAGByCK0AdKVJjaEAgMlheTAAAACNJbQCAADQWEIrAAAAjSW0AgAA0FhCKwAAAI0ltAIAANBYQisAAACNJbQCAADQWEIrAAAAjSW0AgAA0FhCKwAAAI0ltAIAANBYQisAAACNJbQCAADQWEIrAAAAjSW0AgAA0FhCKwAAAI0ltAIAANBYQisAAACNdc+wCwBgcq1vtrO6sZ2bO53MzrSyvDifpYW5YZcFADSI0ArAUKxvtrOytpXO7l6SpL3TycraVpIIrgDAbZYHAzAUqxvbtwPrgc7uXlY3todUEQDQREIrAENxc6dzpnEAYDIJrQAMxexM60zjAMBkEloBGIrlxfm0pqcOjbWmp7K8OD+kigCAJtKICYChOGi2pHswAHASoRWAoVlamBNSAYATWR4MAABAYwmtAAAANJbQCgAAQGMJrQAAADSW0AoAAEBjCa0AAAA0ltAKAABAYzmnFejK+mY7qxvbubnTyexMK8uL887XBACg74RW4FTrm+2srG2ls7uXJGnvdLKytpUkgisAAH1leTBwqtWN7duB9UBndy+rG9tDqggAgEkhtAKnurnTOdM4AAD0itAKnGp2pnWmcQAA6BWhdcKsb7Zz+YnrefCxp3P5ietZ32wPuyRGwPLifFrTU4fGWtNTWV6cH1JFAABMCo2YJohmOtytg+tD92AAAAZNaJ0gJzXTET44zdLCnOsEAICBszx4gmimAwAAjBqhdYJopgMAAIwaoXWCaKYDAACMGntaJ4hmOgAAwKgRWieMZjqTbX2z7UMLAABGitAKE8KRR/3hgwAAgP6ypxUmxElHHnF3Dj4IaO90UvPyBwHrm+1hlwYAMDaEVpgQjjzqPR8EAAD0n9AKE8KRR73ngwAAgP4TWmFCOPKo93wQAADQf0IrTIilhblcu3oxczOtlCRzM61cu3pR06Bz8EEAAED/6R4ME6RJRx6NQ9ddZx8DAPSf0AoM3Dgdv9OkDwIAAMaR5cHAwOm6CwBAt9xphXMah2Wug6brLgAA3XKnFc7hYJlre6eTmpeXua5vtoddWqPpugsAQLeEVjgHy1zvjq67AAB0y/JgOAfLXO+OrrsAAHRLaGVi9WIv6uxMK+0jAqplrqfTdRcAgG5YHjzB1jfbufzE9Tz42NO5/MT1idqH2au9qJa5AgBAfwmtE2rSGwj1ai/q0sJcrl29mLmZVkqSuZlWrl296A4iAAD0iOXBE+qk0DYJgauXe1EtcwUAgP5xp3VCTXoDIUeuAADAaBBaJ9SkhzZ7UQEAYDT0JbSWUj5SSvlkKeXxYx5/fSnlV0opHy2l/Hwp5Yv6UQfHm/TQZi8qAACMhp7vaS2lXE0yVWt9Wynlw6WUh2qtn7lj2nck+VCt9aOllJ9I8k1JfqHXtXA852TaiwoAAKOgH42YriR5av/r60keTnIotNZaP/yKby8k+Xwf6uAUQhsAANB0/VgefG+Sg3NTXkzyxuMmllK+Lsn9tdZPHfP4+0opN0opN55//vneVwoAAECj9SO0vpTkoJvPfce9RinlDUl+LMl3H/dEtdYna62Xaq2XLly40PNCAQAAaLZ+hNZncmtJcJK8Kcnn7pyw33jpqSQrtdZn+1ADAAAAY6AfoXU9yXtLKR9K8u1JfquU8oE75nxPkrck+UullI+VUt7dhzpgbK1vtnP5iet58LGnc/mJ61nfbJ/+hwAAYAT1vBFTrfXFUsqVJI8m+WCt9bkkn75jzk8k+YlevzZMgvXNdlbWttLZ3UuStHc6WVnbShKNtQAAGDt9Oae11vpCrfWp/cAK9NDqxvbtwHqgs7uX1Y3tIVUEAAD905fQCvTPzZ3OmcYBAGCUCa0wYmZnWmcaBwCAUSa0wohZXpxPa3rq0FhreirLi/NDqggAAPqn542YgP46aLa0urGdmzudzM60srw4rwkTAABjSWiFEbS0MCekAgAwESwPBgAAoLGEVgAAABpLaAUAAKCxhFYAAAAaS2gFAACgsYRWAAAAGktoBQAAoLGEVgAAABpLaAUAAKCxhFYAAAAaS2gFAACgsYRWAAAAGktoBQAAoLGEVgAAABpLaAUAAKCx7hl2AfTX+mY7qxvbubnTyexMK8uL81lamBt2WQAAAF0RWsfY+mY7K2tb6ezuJUnaO52srG0lieAKAACMBMuDx9jqxvbtwHqgs7uX1Y3tIVUEAABwNkLrGLu50znTOAAAQNMIrWNsdqZ1pnEAAICmEVrH2PLifFrTU4fGWtNTWV6cH1JFAAAAZ6MR0xg7aLakezAAADCqhNYxt7QwJ6QCAAAjy/JgAAAAGktoBQAAoLGEVgAAABrLnlZeZX2zfabmTWedDwAA0C2hlUPWN9tZWdtKZ3cvSdLe6WRlbStJjgyiZ50PAABwFpYHc8jqxvbtAHqgs7uX1Y3tnswHAAA4C6GVQ27udPo6DgAAcBZCK4fMzrT6Og4AAHAWQiuHLC/OpzU9dWisNT2V5cX5nszvhfXNdi4/cT0PPvZ0Lj9xPeub7b69FgAAMFwaMXHIQfOkbrsBn3X+eWn8BAAAk6XUWoddQ1cuXbpUb9y4MewyGLLLT1xP+4j9snMzrfzjx94xhIoAAIDzKqU8U2u9dNRjlgczUjR+AgCAySK0MlI0fgIAgMkitDJShtH4CQAAGB6NmBgpg278BAAADJfQyshZWpgTUgEAYEJYHgwAAEBjCa0AAAA0luXBnGp9s20PKQAAMBRCKyda32xnZW0rnd29JEl7p5OVta0kEVwBAIC+szyYE61ubN8OrAc6u3tZ3dgeUkUAAMAkEVo50c2dzpnGAQAAeklo5USzM60zjQMAAPSS0MqJlhfn05qeOjTWmp7K8uL8kCoCAAAmiUZMnOig2ZLuwQAAwDAIrZxqaWFOSAUAAIZCaB1jzlcFAABGndA6ppyvCgAAjAONmMaU81UBAIBxILSOKeerAgAA40BoHVPOVwUAAMaB0DqmnK8KAACMA42YxpTzVQEAgHEgtI4x56sCAACjzvJgAAAAGktoBQAAoLGEVgAAABrLntY+W99sa4YEAABwl4TWPlrfbGdlbSud3b0kSXunk5W1rSQRXAEAALpgeXAfrW5s3w6sBzq7e1nd2B5SRQAAAKPFndY+urnTOdP43bIEGQAAGFdCaw/dGR5nXjudF/5g91XzXlNKHnzs6Z4ETEuQAQCAcWZ5cI8chMf2Tic1t8LjS//m32V6qrxq7l6tt+esrG1lfbN9169rCTIAADDOhNYeOSo87n6h5p7XlMzNtFKSTJVXB9jzBsxBLUEGAAAYBqG1B9Y322kfExI7u1/I8uJ8/p8n/qN8odYj5xz3Z7sxO9M60zgAAMAoEVrP6WBZ8En+yi/+VpLjg2TZf567sbw4n9b01KGx1vRUlhfn7+r5AAAAmkRoPaejlgXf6aAZ0/LifF69QDip+89zFuub7Vx+4np+6Gd/I188/ZrMtKZTkszNtHLt6kVNmAAAgLGge/A5nWVp79LCXP7rn/2NIx87yx7UOzsGv/AHu2lNT+VH3/1mYRUAABgr7rSeQ7dLemda07e/nuvBHlQdgwEAgEkhtJ7D+3/ht06dM/2akvf/ua+9/X0v9qDqGAwAAEwKofUcdjq7p86594/ckx/62d/I5SeuZ32znaWFuVy7evH2MTh3swdVx2AAAGBS2NPaRyUvB9v2Tud2l+Glhblz7T1dXpw/tKc10TEYAAAYT32501pK+Ugp5ZOllMfPM6fp7n/t9ImP33kqa6/2nfbibi0AAMAo6Pmd1lLK1SRTtda3lVI+XEp5qNb6mbPOGQX//Td/bZZ/7tPZ3TscT1vTr0ln9wtH/ple7Ts9791aAACAUdCPO61Xkjy1//X1JA/f5ZyUUt5XSrlRSrnx/PPP97jM81tamMvqt73p0B3Pv/HuN+df/NV39qRLMAAAwKTrx57We5McnAXzYpKvvss5qbU+meTJJLl06dKdq20b4bg7nvadAgAAnF8/QutLSQ5uJ96Xo+/mdjNnpB0E2dWN7dzc6WR2ppXlxXlLegEAAM6gH6H1mdxa7vupJG9KclTnoW7mjDz7TgEAAM6nH6F1PcknSimzSd6Z5D2llA/UWh8/Yc6f7kMdAAAAjLieL8uttb6YW42WPpXkkVrrp+8IrEfN+b1e1wEAAMDo68ed1tRaX8jL3YHveg4AAACTbewaIAEAADA+hFYAAAAaS2gFAACgsYRWAAAAGktoBQAAoLGEVgAAABpLaAUAAKCxhFYAAAAaS2gFAACgsYRWAAAAGktoBQAAoLGEVgAAABqr1FqHXUNXSinPJ3l22HXc4UuT/KthFwFxLdIcrkWawrVIU7gWaYqmX4tfWWu9cNQDIxNam6iUcqPWemnYdYBrkaZwLdIUrkWawrVIU4zytWh5MAAAAI0ltAIAANBYQuv5PDnsAmCfa5GmcC3SFK5FmsK1SFOM7LVoTysAAACN5U4rAAAAjSW0AgAA0FhC6ylKKR8ppXyylPL4eebAeZ12nZVSXl9K+ZVSykdLKT9fSvmiQdfI5Oj2fa+U8sZSyuag6mLynOFa/HAp5ZsHVReTp4uf0/eXUn65lPKJUsrfGXR9TI79n72fOOHx6VLKL+1fr989yNrultB6glLK1SRTtda3JZktpTx0N3PgvLq8zr4jyYdqrY8meS7JNw2yRibHGd/3/nqS1mAqY9J0ey2WUt6e5Mtqrb840AKZGF1ei+9N8tO11rcneV0pZSTPy6TZSin3J/mfk9x7wrQfSHJj/3r9s6WU1w2kuHMQWk92JclT+19fT/LwXc6B87qSU66zWuuHa60f3f/2QpLPD6Y0JtCVdPG+V0p5R5Lfz60PUaAfruSUa7GUMp3kJ5N8rpTyLYMrjQlzJae/L/7rJPOllJkkX5HkdwZSGZNmL8m7k7x4wpwrefl6/WSSxn+AIrSe7N4k7f2vX0zyxrucA+fV9XVWSvm6JPfXWj81iMKYSKdej/vL0384yWMDrIvJ081743cm+edJPpjkraWUHxhQbUyWbq7Ff5TkoSQ/mOS3k7wwmNKYJLXWF2utv3fKtJHLL0LryV7Ky8va7svR/391MwfOq6vrrJTyhiQ/lmQk9icwsrq5Hh9L8uO11p1BFcVE6uZaXEjyZK31uSQ/neSRAdXGZOnmWvxrSb6v1vojuRVav2tAtcGdRi6/NL7AIXsmLy/veFOSz93lHDivU6+z/TtbTyVZqbU+O7jSmEDdvO/9mSTfX0r5WJI3l1L+7mBKY8J0cy1+NslX7X99KYn3R/qhm2vxtUkullKmkvypJHUwpcGrjFx+KbX693KcUsqXJPlEkl9N8s4k70nyrlrr4yfM+dNd3JKHM+nyWvwvcutT3E/vD/1ErfVnB10r46+b6/GO+R+rtV4ZXIVMii7fG1+X5O/l1vK36STfVmttH/F0cNe6vBbfmuTvJ/nKJL+e5FtrrS8NoVwmwMHP3v3+El9Ta/3br3jsK5P8cpJ/mORtuZVf9oZUaleE1lPsd+B6NMnH95cW3dUcOC/XGU3ieqQpXIs0hWuRUVJKmc2tu60bo3DDTWgFAACgsexpBQAAoLGEVgAAABpLaAWAMyqlvL6U8iullI+WUn5+v3v3aX/m/aWUK8c8du/+8/xfpZR/UEopParzY714HgAYJqEVAM7uO5J8qNb6aJLnknzTOZ/vvUl+vdb6DUn+MLeOZgEAktwz7AIAYNTUWj/8im8vJPl8KeX9uXWkysNJXp9bQfYPk/zvSaaSlCQfO+Yp20n+Yinl52ut35skpZT7cuvs5S9O8myt9btKKc8k+XySf5vky5L8TJI3J/mS3DrSZbPW+l8d9QKllNcm+akkfyzJVq31++/m7w4Ag+ZOKwDcpVLK1yW5v9b6qf2hr96/W/q/JnlHkvcl+aVa6yNJdo97nlrrLyb50SRrpZS/VUqZSvLlSX48t858fKCU8sYkr03yriR/Msmfz61D4ZPk52qtl5M8WEp5yzEv874k/6zW+vVJvryU8ifv+i8OAAMktALAXSilvCHJjyX57lcM/9T+fz+f5IuSPJjkN/fHbpzwXA8l+T9z667phSR/IbdC7vcm+V+SvCFJK8nv1lpfSvJskr3cunubJM/s//c3kzxwzMvMJ/nW/X2uX5Vk7tS/JAA0gNAKAGe033jpqSQrtdZnX/HQ798x9dkkX7P/9ZtPeMrvTfKttda9JP8st5YEf0+Sn0vynxzxvHd66yte47PHzNlO8jdqrVeSPJ7kd055TgBoBKEVAM7ue5K8JclfKqV8rJTy7mPm/WSS/3j/7uaXnPB8fzPJf7o/761J/kGSjyZZSXJ9f85Jd0b/bCnlHyf57Vrrp0+o5Z2llI8n+b4k//KE5wOAxii11mHXAADcpVLK/5Tk/bXWzw25FADoC6EVAACAxrI8GAAAgMYSWgEAAGgsoRUAAIDGEloBAABoLKEVAACAxhJaAQAAaKz/H0LcUk2Zx2/FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第2行和第3行样本值的交会图\n",
    "data_row2 = df_sonar.iloc[1, :-1]\n",
    "data_row3 = df_sonar.iloc[2, 0:60]\n",
    "\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "plt.scatter(data_row2, data_row3)\n",
    "plt.xlabel(\"2nd Sample\")\n",
    "plt.ylabel(\"3rd Sample\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第2行和第21行样本值的交会图\n",
    "data_row21 = df_sonar.iloc[20, 0:60]\n",
    "\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.scatter(data_row2, data_row21)\n",
    "ax.set_xlabel(\"2nd Sample\")\n",
    "ax.set_ylabel(\"21st Sample\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 列（属性对）之间的交会图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第2列和第3列属性值的交会图\n",
    "data_col2 = df_sonar.iloc[:, 1]\n",
    "data_col3 = df_sonar.iloc[:, 2]\n",
    "\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "plt.scatter(data_col2, data_col3)\n",
    "plt.xlabel(\"2nd Attribution\")\n",
    "plt.ylabel(\"3rd Attribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第2列和第21列属性值的交会图\n",
    "data_col21 = df_sonar.iloc[:, 20]\n",
    "\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.scatter(data_col2, data_col21)\n",
    "ax.set_xlabel(\"2nd Attribution\")\n",
    "ax.set_ylabel(\"21st Attribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果散点图上的点沿着**一条“瘦”直线排列，则说明这两个变量强相关，如果这些点形成一个球形，则说明这些点不相关**（只是在线性相关角度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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